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pro vyhledávání: '"Huang, Hailiang"'
The widespread applications of large language models (LLMs) have brought about concerns regarding their potential misuse. Although aligned with human preference data before release, LLMs remain vulnerable to various malicious attacks. In this paper,
Externí odkaz:
http://arxiv.org/abs/2407.01902
Autor:
Wang, Chaozheng, Li, Zongjie, Gao, Cuiyun, Wang, Wenxuan, Peng, Ting, Huang, Hailiang, Deng, Yuetang, Wang, Shuai, Lyu, Michael R.
Code generation aims to synthesize code and fulfill functional requirements based on natural language (NL) specifications, which can greatly improve development efficiency. In the era of large language models (LLMs), large code models (LCMs) have bee
Externí odkaz:
http://arxiv.org/abs/2404.19368
Autor:
Guo, Biyang, Wang, He, Xiao, Wenyilin, Chen, Hong, Lee, Zhuxin, Han, Songqiao, Huang, Hailiang
In the burgeoning field of Large Language Models (LLMs) like ChatGPT and LLaMA, Prompt Engineering (PE) is renowned for boosting zero-shot or in-context learning (ICL) through prompt modifications. Yet, the realm of the sample design for downstream f
Externí odkaz:
http://arxiv.org/abs/2404.13033
Autor:
Shen, Hanyang, Gelaye, Bizu, Huang, Hailiang, Rondon, Marta B., Sanchez, Sixto, Duncan, Laramie E.
Publikováno v:
Universidad Peruana de Ciencias Aplicadas (UPC)Repositorio Academico - UPCNeuropsychopharmacology.
LED and HS have been funded by startup funds from Stanford and a pilot grant to LED from the Stanford Center for Clinical and Translation Research and Education (UL1 TR001085, PI Greenberg). LED has also been funded by Cohen Veterans Bioscience (CVB)
Externí odkaz:
http://hdl.handle.net/10757/655129
Molecular Property Prediction (MPP) task involves predicting biochemical properties based on molecular features, such as molecular graph structures, contributing to the discovery of lead compounds in drug development. To address data scarcity and imb
Externí odkaz:
http://arxiv.org/abs/2312.03292
Autor:
Jiang, Minqi, Hou, Chaochuan, Zheng, Ao, Han, Songqiao, Huang, Hailiang, Wen, Qingsong, Hu, Xiyang, Zhao, Yue
Deep learning (DL) techniques have recently found success in anomaly detection (AD) across various fields such as finance, medical services, and cloud computing. However, most of the current research tends to view deep AD algorithms as a whole, witho
Externí odkaz:
http://arxiv.org/abs/2309.15376
Recent studies give more attention to the anomaly detection (AD) methods that can leverage a handful of labeled anomalies along with abundant unlabeled data. These existing anomaly-informed AD methods rely on manually predefined score target(s), e.g.
Externí odkaz:
http://arxiv.org/abs/2306.14403
Autor:
Song, Yifan, Xiong, Weimin, Zhu, Dawei, Wu, Wenhao, Qian, Han, Song, Mingbo, Huang, Hailiang, Li, Cheng, Wang, Ke, Yao, Rong, Tian, Ye, Li, Sujian
Tool-augmented large language models (LLMs) have achieved remarkable progress in tackling a broad range of tasks. However, existing methods are mainly restricted to specifically designed tools and fail to fulfill complex instructions, having great li
Externí odkaz:
http://arxiv.org/abs/2306.06624
Autor:
Jiang, Minqi, Hou, Chaochuan, Zheng, Ao, Hu, Xiyang, Han, Songqiao, Huang, Hailiang, He, Xiangnan, Yu, Philip S., Zhao, Yue
Anomaly detection (AD) is a crucial task in machine learning with various applications, such as detecting emerging diseases, identifying financial frauds, and detecting fake news. However, obtaining complete, accurate, and precise labels for AD tasks
Externí odkaz:
http://arxiv.org/abs/2302.04549
Autor:
Wang, Chaozheng, Hu, Junhao, Gao, Cuiyun, Jin, Yu, Xie, Tao, Huang, Hailiang, Lei, Zhenyu, Deng, Yuetang
Code completion has become a common practice for programmers during their daily programming activities. It aims at automatically predicting the next tokens or lines that the programmers tend to use. A good code completion tool can substantially save
Externí odkaz:
http://arxiv.org/abs/2301.03846